Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
This work addresses ECG analysis for medical diagnostics by extending unsupervised learning to this domain, though it is incremental as it adapts existing methods to new data.
The paper tackled ECG arrhythmia classification by proposing an unsupervised pre-training technique using masked autoencoders, achieving 94.39% accuracy on the MITDB dataset and outperforming fully supervised methods on unseen data.
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend unsupervised learning methods to other domains, which helps to utilize a large amount of unlabelled data. This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks. Experiments are conducted using various model architectures and large-scale datasets, resulting in an accuracy of 94.39% on the MITDB dataset for ECG arrhythmia classification task. The result shows a better performance for the classification of previously unseen data for the proposed approach compared to fully supervised methods.